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Proceedings Paper

Two-dimensional chromosomes and their application to computer vision
Author(s): Trent A. Bills; Ashok Samal
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Paper Abstract

Genetic algorithms have been used for many diverse applications. In these applications, possible solutions are represented by linear strings. In many other applications, however, strings cannot adequately model the solutions. A very large class of such problems is in the area of computer vision and image processing. Here the images are 2D and the objects present in them are either 2D or 3D. Hence the strings and the associated genetic operators are not applicable directly. It is necessary to allow the genetic algorithm to operate directly on images or 2D arrays since the underlying processes that are responsible for the formation of the solutions are inherently 2D in nature, e.g. rotation, translation, etc. We have extended the concepts defined for linear strings to 2D chromosomes. Several new concepts have also been developed to describe genetic operators on 2D chromosomes. The traditional genetic operators are also extended and some new geometric operators are also introduced for the 2D chromosomes. A typical computer vision problem is used to demonstrate the use of the operators. A prototype parallel implementation on a CM-2 (SIMD) has been implemented and a CM-5 (MIMD) version is currently being explored.

Paper Details

Date Published: 30 June 1994
PDF: 11 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179241
Show Author Affiliations
Trent A. Bills, Univ. of Nebraska/Lincoln (United States)
Ashok Samal, Univ. of Nebraska/Lincoln (United States)


Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)

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